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Jasbir Dhaliwal,1– 6 Meltem Tuna,1 Baiju R Shah,1,7,8 Sanjay Murthy,1,9,10 Emily Herrett,4 Anne M Griffiths,2,3 Eric I Benchimol1– 3,8 1ICES, Toronto, ON, Canada; 2SickKids Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children, Toronto, ON, Canada; 3Department of Paediatrics, University of Toronto, Toronto, ON, Canada; 4London School of Hygiene and Tropical Medicine, London, UK; 5Cincinnati Children’s Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, Cincinnati, OH, USA; 6Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA; 7Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; 8Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; 9The Ottawa Hospital IBD Centre, The Ottawa Hospital and Ottawa Hospital Research Institute, Ottawa, ON, Canada; 10Department of Medicine and School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, CanadaCorrespondence: Jasbir DhaliwalPediatrics, University of Cincinnati, Cincinnati Children’s Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, Secondary Appointments in Division of Biomedical Informatics and James M Anderson Center for Health Systems Excellence, 3333 Burnet Avenue, Cincinnati, OH, 45229, USATel +15133789468Email Jasbir.dhaliwal@cchmc.orgBackground: Inflammatory bowel disease (IBD) is now a global disease with incidence increasing throughout Asia.Aim: To determine the incidence of IBD among South Asians and Chinese people residing in Ontario, Canada’s most populous province.Methods: All incident cases of IBD in children (1994– 2015) and adults (1999– 2015) were identified from population-based health administrative data. We classified South Asian and Chinese ethnicity using immigration records and surnames. We determined standardized incidence of IBD and adjusted incidence rate ratio (aIRR) in South Asians and Chinese compared to the general population.Results: Among 16,230,638 people living in Ontario, standardized incidence of IBD per 100,000 person-years was 24.7 (95% CI 24.4– 25.0), compared with 14.6 (95% CI 13.7– 15.5) in 982,472 South Asians and with 5.4 (95% CI 4.8– 5.9) in 764,397 Chinese. The risk of IBD in South Asians was comparable to the general population after adjusting for immigrant status and confounders (aIRR 1.03, 95% CI 0.96– 1.10). South Asians had a lower risk of Crohn’s disease (CD) (aIRR 0.66, 95% CI 0.60– 0.77), but a higher risk of ulcerative colitis (UC) (aIRR 1.47, 95% CI 1.34– 1.61). Chinese people had much lower rates of IBD (aIRR 0.24, 95% CI 0.20– 0.28), CD (aIRR 0.21, 95% CI 0.17– 0.26), and UC (aIRR 0.28, 95% CI 0.23– 0.25).Conclusion: Canadians of South Asian ethnicity had a similarly high risk of developing IBD compared to other Canadians, and a higher risk of developing UC, a finding distinct from the Chinese population. Our findings indicate the importance of genetic and environmental risk factors in people of Asian origin who live in the Western world.Keywords: inflammatory bowel disease, ulcerative colitis, Crohn’s disease, incidence, ethnicity, epidemiology, South Asian, Chinese

Introduction Two recent high-profile publications (and subsequent retractions) of pharmacoepidemiology studies reporting the effectiveness and risk of hydroxychloroquine in COVID-19 patients received international media attention. Transparent and complete reporting of these studies could have provided peer reviewers and editors with sufficient information to question the methods used and the validity of results. Since these studies used routinely collected health data, the guidelines for the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) should have been applied to ensure complete reporting of the research. Methods We evaluated the two retracted articles for completeness of reporting using the RECORD for Pharmacoepidemiology (RECORD-PE) checklist, which includes the checklists for the STengthening the Reporting of OBservational studies in Epidemiology (STROBE) and RECORD. We compared the proportion of STROBE, RECORD and RECORD-PE items adequately reported using Chi-squared statistics. Results In the article published by The Lancet, 29 of 34 STROBE items (85.3%) were adequately reported, compared with 3.5 of 13 RECORD items (26.9%) and 9.5 of 15 RECORD-PE items (63.3%)(χ2 = 14.839, P Discussion While STROBE items were generally adequately reported, RECORD and RECORD-PE items were not. Reporting guidelines should be effectively implemented in order for transparency and completeness of research manuscripts, allowing for adequate evaluation by editors and peer reviewers.

Purpose To describe methods used to identify the timing of transfer from pediatric to adult care within health administrative data and to identify the advantages and limitations of each method to guide future research. Study design and settings We conducted a scoping review to identify studies, summarized challenges of identifying the timing of transfer, and proposed methodological approaches for each. Results Studies use the following approaches to capture individuals who transfer from pediatric to adult care by 1) defining the timing of transfer by the last pediatric and first adult care visit last and 2) defining transfer to adult care based on a specific age. Conclusion There are important limitations of administrative data that must be recognized in designing studies examining the transfer to adult care.

Background Type 1 diabetes is one of the most common chronic diseases in childhood with a worldwide incidence that is increasing by 3-5% per year. The incidence of type 2 diabetes, traditionally viewed as an adult disease, is increasing at alarming rates in children, paralleling the rise in childhood obesity. As the rates of diabetes increase in children, accurate population-based assessment of disease burden is important for those implementing strategies for health services delivery. Health administrative data are a powerful tool that can be used to track disease burden, health services use, and health outcomes. Case validation is essential in ensuring accurate disease identification using administrative databases. Aim The aim of our study was to define and validate a pediatric diabetes case ascertainment algorithm (including any form of childhood-onset diabetes) using health administrative data. Research design and methods We conducted a two-stage method using linked health administrative data and data extracted from charts. In stage 1, we linked chart data from a large urban region to health administrative data and compared the diagnostic accuracy of various algorithms. We selected those that performed the best to be validated in stage 2. In stage 2, the most accurate algorithms were validated with chart data within two other geographic areas in the province of Quebec. Results Accurate identification of diabetes in children (ages ≤15 years) required four physician claims or one hospitalization (with International Classification of Disease codes within 1 year (sensitivity 91.2%, 95% confidence interval [CI] 89.2-92.9]; positive predictive value [PPV] 93.5%, 95% CI 91.7-95.0) or using only four physician claims in 2 years (sensitivity 90.4%, 95% CI 88.3-92.2; PPV 93.2%, 95% CI 91.7-95.0). Separating the physician claims by 30 days increased the PPV of all algorithms tested. Conclusion Patients with child-onset diabetes can be accurately identified within health administrative databases providing a valid source of information for health care resource planning and evaluation.

Eric I Benchimol,1–5 M Ellen Kuenzig,1,2,5 Charles N Bernstein,6,7 Geoffrey C Nguyen,5,8 Astrid Guttmann,5,9 Jennifer L Jones,10 Beth K Potter,4 Laura E Targownik,6,7 Christina A Catley,5 Zoann J Nugent,6,11 Divine Tanyingoh,12,13 Nassim Mojaverian,5 Fox E Underwood,12,13 Shabnaz Siddiq,1,2 Anthony R Otley,14 Alain Bitton,15 Matthew W Carroll,16 Jennifer C deBruyn,17 Trevor JB Dummer,18 Wael El-Matary,19 Anne M Griffiths,9 Kevan Jacobson,20,21 Desmond Leddin,10 Lisa M Lix,22 David R Mack,1–3 Sanjay K Murthy,4,23 Juan Nicolás Peña-Sánchez,24 Harminder Singh,6,7 Gilaad G Kaplan12,13 On behalf of the Canadian Gastro-Intestinal Epidemiology Consortium 1Children’s Hospital of Eastern Ontario IBD Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, Ottawa, Canada; 2Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada; 3Department of Pediatrics, University of Ottawa, Ottawa, Canada; 4School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; 5ICES, Toronto, Canada; 6University of Manitoba IBD Clinical and Research Centre, University of Manitoba, Winnipeg, Canada; 7Department of Internal Medicine, University of Manitoba, Winnipeg, Canada; 8Department of Medicine, Mount Sinai Hospital Centre for Inflammatory Bowel Disease, University of Toronto, Toronto, Canada; 9Department of Paediatrics, University of Toronto, Toronto, Canada; 10Department of Medicine, Dalhousie University, Halifax, Canada; 11CancerCare Manitoba, Winnipeg, Canada; 12Department of Medicine, University of Calgary, Calgary, Alberta, Canada; 13Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; 14Department of Pediatrics, Dalhousie University, Halifax, Canada; 15Division of Gastroenterology, McGill University Health Centre, Montreal, Canada; 16Division of Pediatric Gastroenterology and Nutrition, Department of Pediatrics, University of Alberta, Edmonton, Canada; 17Division of Gastroenterology, Department of Pediatrics, University of Calgary, Calgary, Canada; 18School of Population and Public Health, University of British Columbia, British Columbia, Canada; 19Department of Pediatrics, University of Manitoba, Winnipeg, Canada; 20Department of Pediatrics, The University of British Columbia, British Columbia, Canada; 21Child and Family Research Institute, The University of British Columbia, British Columbia, Canada; 22Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada; 23The Ottawa Hospital IBD Centre, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada; 24Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada Background and aims: Canada’s large geographic area and low population density pose challenges in access to specialized health care for remote and rural residents. We compared health services use, surgical rate, and specialist gastroenterologist care in rural and urban inflammatory bowel disease (IBD) patients in Canada.Methods: We used validated algorithms that were applied to population-based health administrative data to identify all people living with the following three Canadian provinces: Alberta, Manitoba, and Ontario (ON). We compared rural residents with urban residents for time to diagnosis, hospitalizations, outpatient visits, emergency department (ED) use, surgical rate, and gastroenterologist care. Multivariable regression compared the outcomes in rural/urban patients, controlling for confounders. Provincial results were meta-analyzed using random-effects models to produce overall estimates.Results: A total of 36,656 urban and 5,223 rural residents with incident IBD were included. Outpatient physician visit rate was similar in rural and urban patients. IBD-specific and IBD-related hospitalization rates were higher in rural patients (incidence rate ratio [IRR] 1.17, 95% CI 1.02–1.34, and IRR 1.27, 95% CI 1.04–1.56, respectively). The rate of ED visits in ON were similarly elevated for rural patients (IRR 1.53, 95% CI 1.42–1.65, and IRR 1.33, 95% CI 1.25–1.40). There were no differences in surgical rates or prediagnosis lag time between rural and urban patients. Rural patients had fewer IBD-specific gastroenterologist visits (IRR 0.79, 95% CI 0.73–0.84) and a smaller proportion of their IBD-specific care was provided by gastroenterologists (28.3% vs 55.2%, P65years (33.0% vs 59.2%, P

Introduction Two recent high-profile publications (and subsequent retractions) of pharmacoepidemiology studies reporting the effectiveness and risk of hydroxychloroquine in COVID-19 patients received international media attention. Transparent and complete reporting of these studies could have provided peer reviewers and editors with sufficient information to question the methods used and the validity of results. Since these studies used routinely collected health data, the guidelines for the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) should have been applied to ensure complete reporting of the research. Methods We evaluated the two retracted articles for completeness of reporting using the RECORD for Pharmacoepidemiology (RECORD-PE) checklist, which includes the checklists for the STengthening the Reporting of OBservational studies in Epidemiology (STROBE) and RECORD. We compared the proportion of STROBE, RECORD and RECORD-PE items adequately reported using Chi-squared statistics. Results In the article published by The Lancet, 29 of 34 STROBE items (85.3%) were adequately reported, compared with 3.5 of 13 RECORD items (26.9%) and 9.5 of 15 RECORD-PE items (63.3%)(χ2 = 14.839, P

Purpose To describe methods used to identify the timing of transfer from pediatric to adult care within health administrative data and to identify the advantages and limitations of each method to guide future research. Study Design and Settings We conducted a scoping review to identify studies, summarized challenges of identifying the timing of transfer, and proposed methodological approaches for each. Results Studies use the following approaches to capture individuals who transfer from pediatric to adult care by 1) defining the timing of transfer by the last pediatric and first adult care visit last and 2) defining transfer to adult care based on a specific age. Conclusion There are important limitations of administrative data that must be recognized in designing studies examining the transfer to adult care.

M Ellen Kuenzig,1–3 Mohsen Sadatsafavi,4 J Antonio Aviña-Zubieta,5 Rebecca M Burne,6 Michal Abrahamowicz,6,7 Marie-Eve Beauchamp,7 Gilaad G Kaplan,8–11,* Eric I Benchimol1–3,12,13,* 1Children’s Hospital of Eastern Ontario (CHEO) Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada; 2Health Information Technology Program, CHEO Research Institute, Ottawa, ON, Canada; 3Institute for Clinical Evaluative Sciences, Ottawa, ON, Canada; 4Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada; 5Arthritis Research Canada, Division of Rheumatology, University of British Columbia, Vancouver, BC, Canada; 6Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; 7Centre for Health Outcomes Research (CORE), Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 8Department of Medicine, University of Calgary, Calgary, AB, Canada; 9Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; 10O’Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada; 11Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada; 12Department of Pediatrics, University of Ottawa; Ottawa, ON, Canada; 13School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada *These authors contributed equally to this work Purpose: Growing evidence suggests asthma and Crohn’s disease commonly cooccur. However, the impact of asthma on the prognosis of Crohn’s disease is unknown. The aim of our study was to assess the effect of asthma on the need for intestinal resection in patients with Crohn’s disease while adjusting for smoking status, imputed from a smaller, secondary data set. Patients and methods: Using health administrative data from a universally funded healthcare plan in Alberta, Canada, we conducted a cohort study to assess the effect of asthma on the need for surgery in patients with Crohn’s disease diagnosed between 2002 and 2008 (N=2,113). Validated algorithms were used to identify incident cases of Crohn’s disease, cooccurring asthma, and intestinal resection. The association between asthma and intestinal resection was estimated using multivariable Cox proportional hazards regression. Smoking status was imputed using a novel method using martingale residuals, derived from a data set of 485 patients enrolled in the Alberta Inflammatory Bowel Disease Consortium (2007 to 2014) who completed environmental questionnaires. All analyses were adjusted for age, sex, rural/urban status, and mean neighborhood income quintile. Results: Asthma did not increase the risk of surgery in the health administrative data when not adjusting for smoking status (HR 1.03, 95% CI 0.81 to 1.29). The association remained nonsignificant after imputing smoking status in the health administrative data (HR 1.03, 95% CI 0.81 to 1.29). Conclusion: Although asthma is associated with an increased risk of Crohn’s disease, co-occurring asthma is not associated with the risk of surgery in these patients. This null association persisted after adjusting for smoking status. This study described a novel method to adjust for confounding (smoking status) in time-to-event analyses, even when the confounding variable is unmeasured in health administrative data. Keywords: Crohn’s disease, asthma, surgery, unmeasured confounding, health administrative data

Mira Y Friedman,1,2 Maya Leventer-Roberts,3 Joseph Rosenblum,4 Nir Zigman,4 Iris Goren,4 Vered Mourad,4 Natan Lederman,5 Nurit Cohen,5 Eran Matz,6 Doron Z Dushnitzky,6 Nirit Borovsky,6 Moshe B Hoshen,3 Gili Focht,1 Malka Avitzour,1 Yael Shachar,1 Yehuda Chowers,7 Rami Eliakim,8 Shomron Ben-Horin,8 Shmuel Odes,9 Doron Schwartz,9 Iris Dotan,10 Eran Israeli,11 Zohar Levi,10 Eric I Benchimol,12–14 Ran D Balicer,3 Dan Turner1 On behalf of the Israeli IBD Research Nucleus (IIRN) 1The Juliet Keidan Institute of Pediatric Gastroenterology and Nutrition, Shaare Zedek Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel; 2Braun School of Public and Community Medicine, The Hebrew University– Hadassah Medical Center, Jerusalem, Israel; 3Clalit Research Institute, Chief’s Office, Clalit Health Services, Tel Aviv, Israel; 4Maccabi Healthcare Services, Tel Aviv, Israel; 5Meuhedet Health Services, Tel Aviv, Israel; 6Leumit Health Services, Tel Aviv, Israel; 7Department of Gastroenterology, Rambam Health Care Campus, Bruce Rappaport School of Medicine, Technion Israel Institute of Technology, Haifa, Israel; 8Department of Gastroenterology, Chaim Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; 9Department of Gastroenterology and Hepatology, Soroka Medical Center, Ben-Gurion University of the Negev, Beer Sheva, Israel; 10Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; 11Institute of Gastroenterology and Liver Diseases, Hadassah Medical Center, Hebrew University, Jerusalem, Israel; 12CHEO Inflammatory Bowel Disease Centre, Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada; 13Department of Pediatrics and School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON, Canada; 14Institute for Clinical Evaluative Sciences, Ottawa, ON, Canada Background: Before embarking on administrative research, validated case ascertainment algorithms must be developed. We aimed at developing algorithms for identifying inflammatory bowel disease (IBD) patients, date of disease onset, and IBD type (Crohn’s disease [CD] vs ulcerative colitis [UC]) in the databases of the four Israeli Health Maintenance Organizations (HMOs) covering 98% of the population. Methods: Algorithms were developed on 5,131 IBD patients and 2,072 controls, following independent chart review (60% CD and 39% UC). We reviewed 942 different combinations of clinical parameters aided by mathematical modeling. The algorithms were validated on an independent cohort of 160,000 random subjects. Results: The combination of the following variables achieved the highest diagnostic accuracy: IBD-related codes, alone if more than five to six codes or combined with purchases of IBD-related medications (at least three purchases or ≥3 months from the first to last purchase) (sensitivity 89%, specificity 99%, positive predictive value [PPV] 92%, negative predictive value [NPV] 99%). A look-back period of 2–5 years (depending on the HMO) without IBD-related codes or medications best determined the date of diagnosis (sensitivity 83%, specificity 68%, PPV 82%, NPV 70%). IBD type was determined by the majority of CD/UC codes of the three recent contacts or the most recent when less than three contacts were recorded (sensitivity 92%, specificity 97%, PPV 97%, NPV 92%). Applying these algorithms, a total of 38,291 IBD patients were residing in Israel, corresponding to a prevalence rate of 459/100,000 (0.46%). Conclusion: The application of the validated algorithms to Israel’s administrative databases will now create a large and accurate ongoing population-based cohort of IBD patients for future administrative studies. Keywords: inflammatory bowel diseases, Crohn’s disease, ulcerative colitis, search algorithms, validation, case ascertainment, Israel, administrative database research

Ahmed Nasr,1,2 Katrina J Sullivan,1 Emily W Chan,1 Coralie A Wong,3 Eric I Benchimol2–5 1Department of Pediatric Surgery, Children’s Hospital of Eastern Ontario, 2Faculty of Medicine, University of Ottawa, 3Institute for Clinical Evaluative Science (ICES University of Ottawa), 4CHEO Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, 5School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada Objective: Incidence rates of Hirschsprung disease (HD) vary by geographical region, yet no recent population-based estimate exists for Canada. The objective of our study was to validate and use health administrative data from Ontario, Canada to describe trends in incidence of HD between 1991 and 2013.Study design: To identify children with HD we tested algorithms consisting of a combination of diagnostic, procedural, and intervention codes against the reference standard of abstracted clinical charts from a tertiary pediatric hospital. The algorithm with the highest positive predictive value (PPV) that could maintain high sensitivity was applied to health administrative data from April 31, 1991 to March 31, 2014 (fiscal years 1991–2013) to determine annual incidence. Temporal trends were evaluated using Poisson regression, controlling for sex as a covariate.Results: The selected algorithm was highly sensitive (93.5%) and specific (>99.9%) with excellent predictive abilities (PPV 89.6% and negative predictive value >99.9%). Using the algorithm, a total of 679 patients diagnosed with HD were identified in Ontario between 1991 and 2013. The overall incidence during this time was 2.05 per 10,000 live births (or 1 in 4,868 live births). The incidence did not change significantly over time (odds ratio 0.998, 95% confidence interval 0.983–1.013, p = 0.80).Conclusion: Ontario health administrative data can be used to accurately identify cases of HD and describe trends in incidence. There has not been a significant change in HD incidence over time in Ontario between 1991 and 2013. Keywords: Hirschsprung disease, algorithm validation, incidence, health administrative data

Meranda Nakhla,1,2 Marc Simard,3 Marjolaine Dube,3 Isabelle Larocque,3 Céline Plante,3 Laurent Legault,1,2 Celine Huot,4 Nancy Gagné,5 Julie Gagné,6 Sarah Wafa,2 Eric I Benchimol,7–9 Elham Rahme10 1Department of Pediatrics, Division of Endocrinology, Montreal Children’s Hospital, Montreal, QC, Canada; 2Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 3Institut National de Santé Publique du Québec, Québec, QC, Canada; 4Department of Pediatrics, Division of Endocrinology, Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada; 5Department of Pediatrics, Division of Endocrinology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; 6Department of Pediatrics, Division of Endocrinology, Centre Hospitalier de l’Université Laval, Quebec City, QC, Canada; 7Children’s Hospital of Eastern Ontario IBD Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, Ottawa, Canada; 8Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada; 9Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; 10Department of Medicine, Division of Clinical Epidemiology, McGill University, Montreal, QC, CanadaCorrespondence: Meranda NakhlaCenter of Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, 5252 de Maisonneuve Blvd, W, 3rd floor, office E3.08, Montreal H4A 3S5, QC, CanadaEmail meranda.nakhla@mcgill.caBackground: Type 1 diabetes is one of the most common chronic diseases in childhood with a worldwide incidence that is increasing by 3–5% per year. The incidence of type 2 diabetes, traditionally viewed as an adult disease, is increasing at alarming rates in children, paralleling the rise in childhood obesity. As the rates of diabetes increase in children, accurate population-based assessment of disease burden is important for those implementing strategies for health services delivery. Health administrative data are a powerful tool that can be used to track disease burden, health services use, and health outcomes. Case validation is essential in ensuring accurate disease identification using administrative databases.Aim: The aim of our study was to define and validate a pediatric diabetes case ascertainment algorithm (including any form of childhood-onset diabetes) using health administrative data.Research design and methods: We conducted a two-stage method using linked health administrative data and data extracted from charts. In stage 1, we linked chart data from a large urban region to health administrative data and compared the diagnostic accuracy of various algorithms. We selected those that performed the best to be validated in stage 2. In stage 2, the most accurate algorithms were validated with chart data within two other geographic areas in the province of Quebec.Results: Accurate identification of diabetes in children (ages ≤15 years) required four physician claims or one hospitalization (with International Classification of Disease codes within 1 year (sensitivity 91.2%, 95% confidence interval [CI] 89.2–92.9]; positive predictive value [PPV] 93.5%, 95% CI 91.7–95.0) or using only four physician claims in 2 years (sensitivity 90.4%, 95% CI 88.3–92.2; PPV 93.2%, 95% CI 91.7–95.0). Separating the physician claims by 30 days increased the PPV of all algorithms tested.Conclusion: Patients with child-onset diabetes can be accurately identified within health administrative databases providing a valid source of information for health care resource planning and evaluation.Keywords: pediatric, validation, health administrative data, diabetes

There has been a surge of availability and use for research of routinely collected electronic health data, such as electronic health records, health administrative data, and disease registries. Symptomatic of this surge, in 2012, Pharmacoepidemiology and Drug Safety (PDS) published a supplemental issue containing several reviews of validated methods for identifying health outcomes using routine health data,1 focusing on databases feeding the US Mini-Sentinel Program.2 In one of the review papers of the PDS Supplement, Carnahan3 acknowledged that while ample validated algorithms exist for major health events, for example, cardiovascular events, validated methods of identifying many health outcomes are lacking. Furthermore, the referenced studies focused on algorithms based on coding sets used in the United States (eg, ICD-9) to identify events from US databases, set within the US health care system. This leaves out an entire segment of routine databases, most notably, Nordic national registries or other European databases such as Clinical Practice Research Datalink (CPRD), The Heatlh Improvement Network (THIN) Hospital Episode Statistics (HES), or PHARMO, all of which are set in health care systems that are differently run and financed than those in the United States. Since other systems function differently, and the databases contain different variables, validation of health status in US data may not always be generalizable.5–9 Many validation studies have been done among these various resources,10–12 but the work is far from complete, as shown in a systematic review of validation studies of the UK-based Clinical Practice Research Datalink, published in 2010.13 Some algorithms may become outdated because of changes in coding or medical practices; new diseases, without clear representation in classification systems, may emerge. Furthermore, in October 2015, the United States adopted ICD-10,14 while ICD-11 is looming on the horizon.15 Clinical Epidemiology has published and continues to publish studies that describe the validity of algorithms in routinely recorded health data, such as validation of medication use in hospitals,16,17 cancer characteristics and complications,18–20 or events related to reproductive and fetal medicine,21,22 to name just a few examples. An “algorithm” in the present context refers to a combination of values of routinely collected variables that allow identification of cases of a given disease or other health event without having to contact or examine the patient. For example, an algorithm based on a combination of diagnostic ICD-10 codes E10-E11 and medication ATC codes A10 may identify patients with diabetes. The commonly evaluated aspects of an algorithm’s validity are positive predictive value (proportion of algorithm-positive patients who truly have the disease of interest) and sensitivity (proportion of patients with the disease of interest who are algorithm-positive), and their counterparts negative predictive value (proportion of algorithm-negative persons without the disease of interest) and specificity (proportion of persons without the disease who are algorithm-negative). Validity of entire data sources is commonly measured by their completeness (proportion of true cases of a disease captured by a data source). A comprehensive review of methods for validating algorithms to identify disease cohorts from health administrative data, with accompanying reporting guidelines for such work, was published by the Journal of Clinical Epidemiology in 2011.23 Clinical Epidemiology is hereby issuing a targeted call for papers that report on results of validation studies. We are interested in publishing both original validation studies and systematic reviews, using various types of reference (“gold”) standards, such as review of medical charts or comparison with other data sources. Several resources are available to guide reporting, including the 2011 guidelines mentioned above,23 as well as the STARD Checklist,24 and the RECORD Checklist.25,26 Please take advantage of these resources in preparing your high-quality submissions. Some may think of validation work as mundane, a mere poor relative of the “real” original research. We subscribe to a different viewpoint. First, misclassification of study variables threatens the validity of research findings.27 Since epidemiologic research is “an exercise in measurement”,28 high-quality original research is unthinkable without accurate or accurately calibrated instruments. In our editorial experience, evidence of data validity is routinely requested by article referees. Second, following from above, results of validation studies allow epidemiologists to assess the extent of misclassification and estimate its impact on the study results. Third, shining the spotlight on validation studies may activate a feedback loop: physicians may become even more motivated to use systematic coding schemes keeping in mind that the data they feed into the routine databases will be used for research that will ultimately benefit their patients. Last, but not least, validation studies are frequently cited. For example, systematic reviews by Khan et al29 and Herrett et al,13 published in 2010, have already received more than 240 and 350 citations, respectively. We hope you find our arguments compelling and look forward to receiving your validation study submissions.

Recent technological developments in recording health care delivery have led to major research opportunities for epidemiologists and others. There has been a dramatic increase in the availability of “routine data” for research purposes, including data from electronic medical records, administrative data for billing purposes, disease registries, and sources of sociodemographic data. Examples of routine data include data from medical records in the UK in the Clinical Practice Research Database, administrative data from Surveillance, Epidemiology and End Results Medicare, and registry data from the Danish National Registry of Patients. The key aspect that differentiates routine data from other research data sources is the reasons for which the data were collected, since routine data are not specifically collected for research purposes. These data are increasingly available from various health care settings and geographic locations. They present innovative, efficient, and cost-effective prospects with which to answer key research questions. However, use of these data for research leads to specific challenges for researchers and for policymakers and clinicians in using studies based on such data. At present, the strengths, limitations, and biases of available routine data sources are unclear. This confusion has been compounded by incomplete or inadequate reporting of this type of research. An example is the reporting of studies undertaken to validate the quality of data derived from electronic health records. Two recent systematic reviews demonstrated poor reporting of validation studies of data from routine data sources, thereby hampering their utility.1,2 In the last few years, increasing emphasis has been placed on improving the quality of reporting research with a view to increasing the usefulness of research findings.3–5 In 2008, an international collaboration produced the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement to improve the reporting of three types of observational research, ie, cross-sectional, case-control, and cohort studies.6 STROBE has been endorsed by more than 100 health journals. Most research using routinely collected health data is observational in design, so STROBE guidelines should apply to these studies. However, due to the general nature of STROBE, specific issues related to reporting research using routinely collected data are not addressed. REporting of studies Conducted using Observational Routinely collected Data (RECORD) was established to explore and address specific reporting issues related to research using routine data. The issues were explored amongst stakeholders during a workshop convened at the Infectious Disease Research Network Primary Care Database Symposium (January 27, 2012, in London, UK). Over 100 people participated in the workshop, including five conveners of the STROBE initiative. Strong interest was expressed in the idea of developing a reporting guideline specific to research using routinely collected health data. There was general agreement that specific areas related to the reporting of research based on routinely collected health data warranted an extension of the STROBE statement. Some of the specific issues discussed at the Infectious Disease Research Network meeting, which are likely to be major themes when using routinely collected data, included description of database characteristics, validation of diagnostic codes and algorithms to identify exposures and outcomes, and record linkage methodology. These issues are not specifically covered in the STROBE checklists. Therefore, we are proceeding with a formal guideline development process, in close collaboration with members of the STROBE group, to ensure consistent methods and to make this a valuable addition to the STROBE statement. The RECORD initiative was created as an international collaborative process. The RECORD initiative aims to develop an extension of the STROBE checklist to enhance specifically transparent reporting of studies based on routine data sources. The steering committee of the RECORD initiative has been selected to ensure a broad representation of people with expertise and experience in the use of different data types and sources, and representing diverse geographical locations and with experience in rigorous development of reporting guidelines. In addition, a working group with a wide range of expertise will be appointed to inform the development of the guidelines. A broadly representative group of stakeholders will be used to contribute to a modified Delphi consensus which will be a key component of the RECORD guideline development. If readers of this editorial feel they could usefully contribute at a stakeholder level, please visit the website (http://www.record-statement.org). Your input is most welcome. Following the brainstorming session at the Primary Care Database Symposium in January 2012, a search of the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network reporting guidelines library was carried out. The EQUATOR Network (http://www.equator-network.org) is an international initiative committed to improving and upholding transparent and accurate reporting of scientific publications; the network hosts a comprehensive database of reporting guidelines.7 Three checklists were identified that aim to aid in the conduct and critical appraisal of studies using routinely collected data for comparative effectiveness research.8–10 However, these guidelines focus primarily on research methods rather than specifically reporting of such research, they are focused on comparative effectiveness research and therefore do not apply to most observational research; additionally they have not been widely accepted by journal editors and were not developed with international stakeholder consensus or as part of a formal extension of STROBE. Formal reporting guidelines for studies using routinely collected health data, with multidisciplinary expert stakeholder involvement and collaboration from STROBE authors, will help ensure the transparency of the methods used in this growing field of research. Development of the RECORD guidelines will follow the process proposed by the EQUATOR network as previously used in developing the STROBE and other reporting guidelines.11–14 Members of the EQUATOR executive are also members of the steering and working committees for RECORD. The EQUATOR Network recommends five phases to develop a reporting guideline optimally, including initial steps, pre-meeting activities, a face-to-face consensus meeting, post-meeting activities, and post-publication activities. The initial steps include providing evidence about the quality of routinely collected data reports and undertaking a Delphi exercise. Both of these activities should be completed and presented during a face-to-face meeting of the working group and other invited stakeholders. To facilitate uptake, dissemination, and other implementation strategies, the creation of a website is recommended (http://www.record-statement.org). The website can also act as an important portal for communication by interested parties and will enable invitation of criticisms and recommendations to improve the guideline. Dissemination, endorsement, and implementation of the RECORD guidelines will be critical for them to have impact. As such energy will be invested into maximizing the publication strategy and encouraging endorsement and adherence to the guidelines. This will start from an early stage of guideline development by working closely with journal editors from the outset of the initiative. Attendance and presentations at relevant meetings of methodologists and journal editors will be key to the success of the process. We will work closely with editors to ensure endorsement and implementation of the guidelines and encourage individuals submitting research derived using routine data sources to use the RECORD guidelines. We anticipate evaluating whether the use of RECORD improves the completeness of reporting of routinely collected data research. We encourage others to do likewise. RECORD will set the standard for improving the quality of reporting of research using routine data sources. Furthermore, the tool could help researchers to review the available literature and improve areas of methodological concern, thereby improving the quality of research produced from routinely collected health data. Most importantly, the adequate reporting of research will allow consumers of such research (including journal editors, peer reviewers, scientists, clinicians, and policy makers) to understand the work, enabling them to deduce its internal and external validity. The introduction of the RECORD guidelines is likely to enhance the knowledge and skills of those using the data and those undertaking the research in academia or industry. Transparent reporting is critical to increase public engagement with science.